Triple
T9574773
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | MyNetworkTV |
E231017
|
entity |
| Predicate | formerProgrammingModel |
P21929
|
FINISHED |
| Object | telenovela-format prime-time schedule |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: telenovela-format prime-time schedule | Statement: [MyNetworkTV, formerProgrammingModel, telenovela-format prime-time schedule]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: formerProgrammingModel Context triple: [MyNetworkTV, formerProgrammingModel, telenovela-format prime-time schedule]
-
A.
formerProgram
Indicates that an entity previously held the role or status of a program but no longer does so.
-
B.
usedProgramModel
chosen
Indicates that an entity employed a specific program model as the basis or framework for its activities or operations.
-
C.
formerProgrammingFocus
Indicates that an entity previously concentrated on a particular programming-related area or activity, but no longer does so.
-
D.
formerBusinessModel
Indicates that an entity previously operated under a particular business model, but no longer does so.
-
E.
executionModel
Indicates how a process, task, or operation is carried out or implemented, specifying the underlying method, strategy, or mechanism of its execution.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69ca848091c48190bc313d6620d09555 |
completed | March 30, 2026, 2:11 p.m. |
| NER | Named-entity recognition | batch_69cd99aa964c8190a50717edff30f10b |
completed | April 1, 2026, 10:18 p.m. |
| PD | Predicate disambiguation | batch_69ccd59b960c8190966a8870a2426bd5 |
completed | April 1, 2026, 8:21 a.m. |
Created at: March 30, 2026, 8:05 p.m.